{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "amended-composer",
   "metadata": {},
   "source": [
    "框架\n",
    "\n",
    "    一、导入数据\n",
    "    二、原始序列的检验\n",
    "    三、一阶差分序列的检验\n",
    "    四、定阶（参数调优)\n",
    "    五、建模与预测\n",
    "    \n",
    "    参考材料：《Python数据分析与挖掘实践》"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quarterly-measurement",
   "metadata": {},
   "source": [
    "## 工具准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "short-partner",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import json\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "from matplotlib.pylab import style #自定义图表风格\n",
    "style.use('ggplot')\n",
    "\n",
    "# 解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']\n",
    "# 解决坐标轴刻度负号乱码\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "#pip install statsmodels\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf  #自相关图、偏自相关图\n",
    "from statsmodels.tsa.stattools import adfuller as ADF #平稳性检验\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox #白噪声检验\n",
    "import statsmodels.api as sm #D-W检验,一阶自相关检验\n",
    "from statsmodels.graphics.api import qqplot #画QQ图,检验一组数据是否服从正态分布\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-metabolism",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "liquid-listening",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sg11244\n"
     ]
    }
   ],
   "source": [
    "with open('../saleByTopFromSeries.json','r',encoding='utf-8') as f:\n",
    "    listRS = json.load(f)\n",
    "index = 200\n",
    "pyearAndMonth = listRS[index][\"yearAndMonth\"]\n",
    "yearAndMonth = pyearAndMonth[:42]\n",
    "psalesData= listRS[index][\"salesData\"]\n",
    "salesData = psalesData[:42]\n",
    "series = listRS[index][\"series\"]\n",
    "seriesId = listRS[index][\"seriesId\"]\n",
    "\n",
    "print(seriesId)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "mounted-bearing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建字典对象\n",
    "pdata = {\"date\":pyearAndMonth,\"sale\":psalesData}\n",
    "\n",
    "# 创建dataframe\n",
    "psale = pd.DataFrame(pdata)\n",
    "\n",
    "# # 转换日期数据类型\n",
    "# index = pd.DatetimeIndex(yearAndMonth)\n",
    "\n",
    "# 将日期设置索引值\n",
    "psale.set_index(['date'],inplace=True)\n",
    "\n",
    "# 转换销售量数据类型\n",
    "psale.sale=psale.sale.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "decreased-retreat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 历史数据长度\n",
    "g_datalen = len(salesData)\n",
    "\n",
    "g_datalen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sonic-breakdown",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 42 entries, 2017/10 to 2021/3\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   sale    42 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 672.0+ bytes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017/10</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017/11</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017/12</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/1</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/2</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sale\n",
       "date         \n",
       "2017/10   0.0\n",
       "2017/11   0.0\n",
       "2017/12   0.0\n",
       "2018/1    0.0\n",
       "2018/2    0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建字典对象\n",
    "data = {\"date\":yearAndMonth,\"sale\":salesData}\n",
    "\n",
    "# 创建dataframe\n",
    "sale = pd.DataFrame(data)\n",
    "\n",
    "# # 转换日期数据类型\n",
    "# index = pd.DatetimeIndex(yearAndMonth)\n",
    "\n",
    "# 将日期设置索引值\n",
    "sale.set_index(['date'],inplace=True)\n",
    "\n",
    "# 转换销售量数据类型\n",
    "sale.sale=sale.sale.astype('float')\n",
    "\n",
    "primitiveSale = sale\n",
    "\n",
    "# 查看数据信息|\n",
    "sale.info()\n",
    "sale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "searching-assistant",
   "metadata": {},
   "source": [
    "## 原始序列的检验"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "asian-poison",
   "metadata": {},
   "source": [
    "#### 单位根检验，满足该条件，则拒绝原假设（非平稳序列），说明是平稳序列\n",
    "ADF(sale.sale)[1]<0.05\n",
    "#### 白噪声检验，满足该条件，则拒绝原假设（纯随机序列），说明是非白噪声\n",
    "acorr_ljungbox(sale,lags=1)['lb_pvalue'].values[0]<0.05\n",
    "#### 进行差分运算\n",
    "sale=sale.diff(periods=1, axis=0).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "separate-chase",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------第1次计算---------------------:\n",
      " 单位根检验： (-1.6351414650789209, 0.464800629635553, 1, 40, {'1%': -3.6055648906249997, '5%': -2.937069375, '10%': -2.606985625}, 5.023183712513571) \n",
      "白噪声检验：      lb_stat     lb_pvalue\n",
      "1  29.264266  6.314957e-08\n",
      "---------------------第2次计算---------------------:\n",
      " 单位根检验： (-10.14696283224991, 8.138086313646804e-18, 0, 40, {'1%': -3.6055648906249997, '5%': -2.937069375, '10%': -2.606985625}, 10.953094930716283) \n",
      "白噪声检验：     lb_stat  lb_pvalue\n",
      "1  9.389739   0.002182\n",
      "\n",
      "经历了1阶差分\n"
     ]
    }
   ],
   "source": [
    "d = 0\n",
    "while(True):\n",
    "    print(\"---------------------第{}次计算---------------------:\\n\".format(str(d+1)),\n",
    "          \"单位根检验：\",ADF(sale.sale),\"\\n白噪声检验：\",acorr_ljungbox(sale,lags=1))\n",
    "    if (ADF(sale.sale)[1]<0.05) and (acorr_ljungbox(sale,lags=1)['lb_pvalue'].values[0]<0.05):\n",
    "        break\n",
    "    elif d>9:\n",
    "        break\n",
    "    else:\n",
    "        d = d+1\n",
    "        sale=sale.diff(periods=1, axis=0).dropna()\n",
    "print(\"\\n经历了{}阶差分\".format(str(d)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "descending-secondary",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  FutureWarning,\n",
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(sale,lags=g_datalen-1-d).show()\n",
    "plot_pacf(sale,lags=g_datalen/2-1-d).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "union-television",
   "metadata": {},
   "source": [
    "## 建模及预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "knowing-moisture",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>sale</td>       <th>  No. Observations:  </th>  <td>42</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(0, 1, 1)</td>  <th>  Log Likelihood     </th> <td>0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 20 Nov 2021</td> <th>  AIC                </th> <td>3.913</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>17:07:24</td>     <th>  BIC                </th> <td>7.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>10-01-2017</td>    <th>  HQIC               </th> <td>5.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 03-01-2021</td>   <th>                     </th>   <td> </td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>   <td> </td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.4700</td> <td>    0.141</td> <td>   -3.339</td> <td> 0.001</td> <td>   -0.746</td> <td>   -0.194</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0581</td> <td>    0.007</td> <td>    8.285</td> <td> 0.000</td> <td>    0.044</td> <td>    0.072</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.52</td> <th>  Jarque-Bera (JB):  </th> <td>150.59</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.47</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>5.39</td> <th>  Skew:              </th>  <td>-2.35</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>11.13</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                   sale   No. Observations:                   42\n",
       "Model:                 ARIMA(0, 1, 1)   Log Likelihood                   0.044\n",
       "Date:                Sat, 20 Nov 2021   AIC                              3.913\n",
       "Time:                        17:07:24   BIC                              7.340\n",
       "Sample:                    10-01-2017   HQIC                             5.161\n",
       "                         - 03-01-2021                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ma.L1         -0.4700      0.141     -3.339      0.001      -0.746      -0.194\n",
       "sigma2         0.0581      0.007      8.285      0.000       0.044       0.072\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.52   Jarque-Bera (JB):               150.59\n",
       "Prob(Q):                              0.47   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               5.39   Skew:                            -2.35\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        11.13\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(p, q) =(sm.tsa.arma_order_select_ic(sale,max_ar=3,max_ma=3,ic='aic')['aic_min_order'])\n",
    "(p, q)\n",
    "#创建模型\n",
    "model=ARIMA(primitiveSale,order=(p,d,q)).fit()\n",
    "#查看模型报告\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aware-hardware",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "未来6月的销量预测：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2021-04-01    1.181145\n",
       "2021-05-01    1.181145\n",
       "2021-06-01    1.181145\n",
       "2021-07-01    1.181145\n",
       "2021-08-01    1.181145\n",
       "2021-09-01    1.181145\n",
       "Freq: MS, Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#预测\n",
    "print('未来6月的销量预测：')\n",
    "forecast = model.forecast(6) #预测、标准差、置信区间\n",
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "friendly-railway",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1274f178470>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1274f18e9e8>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1274f16d518>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1274f19b438>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '宝马X3汽车销量预测')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pd.DatetimeIndex(psale.index), psale.sale, label=\"原始数据\",color=\"b\")\n",
    "plt.plot(pd.DatetimeIndex(primitiveSale.index), primitiveSale.sale, label=\"阉割数据\",color=\"g\")\n",
    "plt.plot(forecast.index, forecast.values, label=\"预测结果\",color=\"r\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.title(series + \"汽车销量预测\")  # 图形标题\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "random-phone",
   "metadata": {},
   "outputs": [],
   "source": [
    "# psale.index = pd.DatetimeIndex(psale.index)\n",
    "# psale.plot()\n",
    "# plt.title(series+\"汽车原始图像\") # 图形标题\n",
    "# # plt.savefig(\"./img/\"+seriesId+'bf_sale.jpg')\n",
    "# plt.show()\n",
    "\n",
    "# data=pd.concat((primitiveSale,forecast),axis=0)\n",
    "# data.columns=['销量','未来6月销量']\n",
    "# data.index = pd.DatetimeIndex(data.index)\n",
    "# data.plot()\n",
    "# plt.title(series+\"汽车销量预测\") # 图形标题\n",
    "# # plt.rcParams['savefig.dpi'] = 300 #图片像素\n",
    "# # plt.rcParams['figure.dpi'] = 300\n",
    "# # plt.savefig(\"./img/\"+seriesId+'af_sale.jpg')\n",
    "# plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "coordinate-trade",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# data=pd.concat((primitiveSale,forecast,),axis=0)\n",
    "# data.columns=['销量','未来6月销量']\n",
    "# data.index = pd.DatetimeIndex(data.index)\n",
    "# data.plot()\n",
    "# plt.title(\"福特汽车销量预测\") # 图形标题\n",
    "\n",
    "# plt.savefig('sale.jpg')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "assumed-method",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \"\"\"\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  FutureWarning,\n",
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resid=model.resid\n",
    "plt.rcParams['savefig.dpi'] = 100 #图片像素\n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "#自相关图\n",
    "plot_acf(resid,lags=g_datalen-1-d).show()\n",
    "\n",
    "#解读：有短期相关性，但趋向于零。\n",
    "\n",
    "#偏自相关图\n",
    "plot_pacf(resid,lags=g_datalen/2-1-d).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "apart-generator",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qqplot(resid, line='q', fit=True).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "neural-rebate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D-W检验的结果为： 2.0969679226543043\n"
     ]
    }
   ],
   "source": [
    "print('D-W检验的结果为：',sm.stats.durbin_watson(resid.values))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "twenty-headquarters",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "残差序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  0.514232   0.473312\n"
     ]
    }
   ],
   "source": [
    "# 方法一\n",
    "print('残差序列的白噪声检验结果为：',acorr_ljungbox(resid,lags=1))#返回统计量、P值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "union-breeding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1274f337780>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1274f3c5e80>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x127503b93c8>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x127503b9470>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '宝马X3汽车销量预测')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pd.DatetimeIndex(psale.index), psale.sale, label=\"原始数据\",color=\"b\")\n",
    "plt.plot(pd.DatetimeIndex(primitiveSale.index), primitiveSale.sale, label=\"阉割数据\",color=\"g\")\n",
    "plt.plot(forecast.index, forecast.values, label=\"预测结果\",color=\"r\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.title(series + \"汽车销量预测\")  # 图形标题\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "confidential-pursuit",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "becoming-appreciation",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
